Projects
Assignments
Overview
This course equips business professionals with the essential skills to leverage data effectively, addressing business challenges through data science techniques. Participants will learn how to analyze, manipulate, and present data within a structured framework, driving informed decision-making and enhancing business value.
Objectives
By the end of this course, leaner will be able to:
- Apply data science principles to tackle business issues.
- Execute the extract, transform, and load (ETL) process for dataset preparation.
- Employ various techniques to analyze data and extract insights.
- Design machine learning approaches for solving business problems.
- Train, tune, and evaluate classification models.
- Train, tune, and evaluate regression and forecasting models.
- Train, tune, and evaluate clustering models.
- Finalize data science projects by presenting models, deploying them into production, and monitoring performance.
Prerequisites
- High-level understanding of fundamental data science concepts.
- Familiarity with data science lifecycle, roles, and types of data.
- Proficiency in programming, particularly in Python.
- Experience with Python data science libraries like NumPy and pandas.
- Familiarity with databases and SQL querying.
Course Outline
Module 1: Addressing Business Issues with Data ScienceCourses2024-02-19T21:30:07+05:30
- Initiating a data science project.
- Formulating data science problems.
Module 2: Analyzing DataCourses2024-02-19T21:32:19+05:30
- Examining data.
- Exploring data distributions.
- Visualizing and preprocessing data.
Module 3: Designing a Machine Learning ApproachCourses2024-02-19T21:34:59+05:30
- Identifying machine learning concepts.
- Testing hypotheses.
Module 4: Developing Classification ModelsCourses2024-02-19T21:39:18+05:30
- Training, tuning, and evaluating classification models.
Module 5: Finalizing a Data Science ProjectCourses2024-02-19T21:43:25+05:30
- Communicating results to stakeholders.
- Demonstrating models in a web app.
- Implementing and testing production pipelines.
